Evaluation of the reduced state-variable TOMGRO model using boundary data

Shamshiri, R. and Ahmad, D. and Zakaria, A. and Wan Ismail, W.I. and Man, H.C. and Yamin, M. (2016) Evaluation of the reduced state-variable TOMGRO model using boundary data. In: 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, 17-20 Jul 2016, United States.

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Abstract

Crop growth models are often sensitive to boundary inputs and may cause inaccurate simulation results. The objective of this study was to evaluate parameter robustness of the reduced state-variable TOMGRO model (Jones et al. 1999) for yield estimation of tomato in a random greenhouse. TOMGRO was first implemented in Matlab Simulink in order to create a flexible platform for easier interfacing with the inputs and outputs. The final Simulink block was validated with the Lakecity datasets of Jones et al. (1999). An experiment was carried out in an empty glass panels covered greenhouse under tropical lowlands climate condition by turning off all ventilation and cooling systems for creating an adverse microclimate scenario with zero yield expectation (boundary data). Hourly measurements of air temperature (T) and solar radiation were continuously collected for 254 days. The average, minimum and maximum T values during the entire experiment were equal to 34.5, 22.5, and 68.3°C, corresponding to simulated growth response of zero between hours of 12:00 and 18:00. Results of simulation with TOMGRO model showed that the estimated total above ground dry weight (WT), total fruit dry weight (WF), and mature fruit dry weight (WM) were equal to 0.576, 0.085 and 0.072 kg/m2 respectively. Based on the consistency of the low estimated fruit yield with the simulated growth responses, the hypothesis that the simplified TOMGRO model with its initial parameters is not capable of estimating tomato yield for a random greenhouse in a different geographical location was rejected. Further investigation of the Lakecity experiment datasets in Jones et al. (1999) showed that data of the same type from random days were highly correlated with R2=0.998.

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